Spatial optimization of watershed management practices for nitrogen load reduction using a modeling-optimization framework.
Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We develo...
| Publicado en: | Journal of Environmental Management Vol. 161; pp. 252 - 261 |
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| Autores principales: | , |
| Formato: | Artículo |
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Academic Press Inc.
Sep2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=109126076&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 109126076 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Sep2015 vid: 161 pid: 735 pub: Academic Press Inc. artinfo: ui: 109126076 10.1016/j.jenvman.2015.06.052 ppf: 252 ppct: 9 formats: tig: atl: Spatial optimization of watershed management practices for nitrogen load reduction using a modeling-optimization framework. aug: au: Yang, Guoxiang Best, Elly P.H. affil: Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Research Associate, U.S. Environmental Protection Agency, National Risk Management Research Laboratory, 26 West Martin Luther King Drive, Cincinnati, OH 45268, USA U.S. Environmental Protection Agency, National Risk Management Research Laboratory, 26 West Martin Luther King Drive, Cincinnati, OH 45268, USA su: Watershed management Mathematical optimization Eutrophication Spatial analysis (Statistics) Genetic algorithms sug: subj: Watershed management Mathematical optimization Eutrophication Spatial analysis (Statistics) Genetic algorithms keyword: Best management practices Buffer strips Genetic algorithm Modeling-optimization Multi-objective optimization Nitrogen-loading Wetland restoration Best management practices Buffer strips Genetic algorithm Modeling-optimization Multi-objective optimization Nitrogen-loading Wetland restoration ab: Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We developed a novel modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. This was accomplished by integrating a GIS-based BMP siting method, a WQM-TMDL-N modeling approach to estimate total nitrogen (TN) loading, and a multi-objective optimization algorithm. Wetland restoration and buffer strip implementation were the two BMP categories used to explore the performance of this framework, both differing greatly in complexity of spatial analysis for site identification. Minimizing TN load and BMP cost were the two objective functions for the optimization process. The performance of this framework was demonstrated in the Tippecanoe River watershed, Indiana, USA. Optimized scenario-based load reduction indicated that the wetland subset selected by the minimum scenario had the greatest N removal efficiency. Buffer strips were more effective for load removal than wetlands. The optimized solutions provided a range of trade-offs between the two objective functions for both BMPs. This framework can be expanded conveniently to a regional scale because the NHDPlus catchment serves as its spatial computational unit. The present study demonstrated the potential of this framework to find cost-effective solutions to meet a water quality target, such as a 20% TN load reduction, under different conditions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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